class_docstr: Running entropy.
  
  Parameters
  ----------
  fading_factor
      Fading factor.
  eps
      Small value that will be added to the denominator to avoid division by zero.
  
  Attributes
  ----------
  entropy : float
      The running entropy.
  n : int
      The current number of observations.
  counter : collections.Counter
      Count the number of times the values have occurred
  
  Examples
  --------
  
  >>> import math
  >>> import random
  >>> import numpy as np
  >>> from scipy.stats import entropy
  >>> from river import stats
  
  >>> def entropy_list(labels, base=None):
  ...     value,counts = np.unique(labels, return_counts=True)
  ...     return entropy(counts, base=base)
  
  >>> SEED = 42 * 1337
  >>> random.seed(SEED)
  
  >>> entro = stats.Entropy(fading_factor=1)
  
  >>> list_animal = []
  >>> for animal, num_val in zip(['cat', 'dog', 'bird'],[301, 401, 601]):
  ...     list_animal += [animal for i in range(num_val)]
  >>> random.shuffle(list_animal)
  
  >>> for animal in list_animal:
  ...     entro.update(animal)
  
  >>> print(f'{entro.get():.6f}')
  1.058093
  >>> print(f'{entropy_list(list_animal):.6f}')
  1.058093
  
  References
  ----------
  [^1]: [Sovdat, B., 2014. Updating Formulas and Algorithms for Computing Entropy and Gini Index from Time-Changing Data Streams. arXiv preprint arXiv:1403.6348.](https://arxiv.org/pdf/1403.6348.pdf)
code: def get(self):
      return self.entropy
  
file_path: river/river/stats/entropy.py
imports: from __future__ import annotations
  import collections
  import math
  from river import stats
label_desc: Use image node_id 4 for calling the Entropy obj's underlying member method code with example usage: obj.get() and returns: self